Blueberry bruise non-destructive detection based on hyperspectral information fusion combined with multi-strategy improved Beluga Whale Optimization algorithm

Mechanical damage significantly reduces the market value of fruits, making the early detection of such damage a critical aspect of agricultural management. This study focuses on the early detection of mechanical damage in blueberries (variety: Sapphire) through a non-destructive method. The proposed...

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Published in:Frontiers in plant science Vol. 15; p. 1411485
Main Authors: Sun, Xiaoxiong, Zhu, Liangkuan, Liu, Dayang
Format: Journal Article
Language:English
Published: Switzerland Frontiers Media SA 19.08.2024
Frontiers Media S.A
Subjects:
Accuracy
Agricultural management
Algorithms
beluga whale optimization algorithm
Berries
Blueberries
Bruising
Classification
Computer vision
Damage detection
Data integration
Discriminant analysis
Environmental conditions
Feature extraction
Food quality
Fruits
Genetic algorithms
Hyperspectral imaging
information fusion
Light intensity
Luminous intensity
Market value
model optimization
Morphology
multi-strategy
Nondestructive testing
Optimization
Plant Science
Sapphire
Seeds
Signal processing
Spectrum analysis
Support vector machines
Vaccinium
beluga whale optimization algorithm
information fusion
feature extraction
multi-strategy
model optimization
ISSN:1664-462X, 1664-462X
Online Access:Get full text
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Summary:Mechanical damage significantly reduces the market value of fruits, making the early detection of such damage a critical aspect of agricultural management. This study focuses on the early detection of mechanical damage in blueberries (variety: Sapphire) through a non-destructive method. The proposed method integrates hyperspectral image fusion with a multi-strategy improved support vector machine (SVM) model. Initially, spectral features and image features were extracted from the hyperspectral information using the successive projections algorithm (SPA) and Grey Level Co-occurrence Matrix (GLCM), respectively. Different models including SVM, RF (Random Forest), and PLS-DA (Partial Least Squares Discriminant Analysis) were developed based on the extracted features. To refine the SVM model, its hyperparameters were optimized using a multi-strategy improved Beluga Whale Optimization (BWO) algorithm. The SVM model, upon optimization with the multi-strategy improved BWO algorithm, demonstrated superior performance, achieving the highest classification accuracy among the models tested. The optimized SVM model achieved a classification accuracy of 95.00% on the test set. The integration of hyperspectral image information through feature fusion proved highly efficient for the early detection of bruising in blueberries. However, the effectiveness of this technology is contingent upon specific conditions in the detection environment, such as light intensity and temperature. The high accuracy of the optimized SVM model underscores its potential utility in post-harvest assessment of blueberries for early detection of bruising. Despite these promising results, further studies are needed to validate the model under varying environmental conditions and to explore its applicability to other fruit varieties.
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Ilahy Riadh, Institut National de la Recherche Agronomique de Tunisie (INRAT), Tunisia
Edited by: Julio Nogales-Bueno, Universidad de Sevilla, Spain
Reviewed by: José Luis Hernández-Hernández, Chilpancingo Institute of Technology, Mexico
ISSN:1664-462X
1664-462X
DOI:10.3389/fpls.2024.1411485